Modern healthcare requires the provision of services by many healthcare workers to many patients. In order to accomplish this, healthcare delivery has been organized into specialized departments or healthcare sources such as, for example, nursing, laboratory, pharmacy, and radiology departments. Each department has responsibility for accomplishing its particular, often specialized, subset of tasks. Sometimes the departments are associated with different healthcare enterprises or offices having different geographic locations. Unfortunately, this has resulted in sub-optimal healthcare operations because patient information related to a single patient that is stored at various departments is not easily accessible from a single place.
Present healthcare information systems (HIS) combine the patient related information for a particular patient from multiple, different healthcare sources into a single consolidated database, having a master patient index (MPI), using various record matching techniques. However, many problems are encountered in trying to match patient information, received from the multiple, different healthcare sources, to a particular patient. Sometimes the record matching techniques incorrectly combines patient information that is not related to the same patient (i.e., a “false match”), combines the same patient information more than one time for the same patient (i.e., a “duplicate match”), and does not combine patient information that was related to the same patient (i.e., a “miss match”). Present record matching techniques generate a correct match for over 90% of the stored patient information, generate a duplicate match for 5% to 10% of the stored patient information, and generate less than 5% of miss matches of the stored patient information. The reliability of the present record matching techniques reduces the confidence level of users of the MPI, especially when relied upon for the delivery of healthcare. Hence, the present record matching techniques generate an unacceptable number of false matches, duplicate matches, and miss matches.
Present systems require computer servers, having large memory capacity and powerful processors, which are expensive. The large memory capacity stores the patient information for each patient that is received from the multiple, different healthcare sources. The memory in the computer servers stores a copy of the patient information received from the multiple, different healthcare sources. Hence, the memory in the computer server must be as large as the combined memory storage capacity of each of the multiple, different healthcare sources. Such a large memory capacity is expensive. The processor must be powerful enough to combine, by adding, updating, purging, and matching, etc., a large amount of patent information received from multiple, different healthcare sources. Such a processor that can handle such complex and computer intensive tasks is also expensive.
Because of the memory and processor demands on the computer servers, various approaches have been taken to efficiently operate the HIS. For example, one approach is to permit the computer server to receive only a small subset of the available patient information for processing and storage responsive to such limitations such as date, healthcare source, type of illness, etc. Another approach is to standardize the collection of the patent information, using recommended minimum memory capacity, to reduce the number of duplicate matches.
Another problem with present systems is the format of the patient information and clinical result data. For example, a White Blood Count may be called a ‘WBC,’ a ‘White Count,’ or a ‘WC’ at the multiple, different hospital sources. Present systems use stored conversion tables or other techniques to create a common format for the MPI. Although the conversion tables and other techniques are generally successful, they do not provide a 100% correct translation. Clinical result data also needs to be combined and has similar combination problems such as data format, units of measure, normal ranges, and other related significant medical information.
Present systems typically update the MPI on a non-real time basis using various download techniques, such as batch, magnetic tape, diskette, batch direct communications, resulting in a MPI that is not current. Usually, real time updates are prevented by the amount of work for the computer server to combine, translate, index, and match the patient information, as well as particular interface implementations, communication methods, database capabilities, and design implementations.
Some present systems require efficient cooperation between the computer server and the computers located at the multiple, different healthcare sources to provide detailed mapping required for implementing the MPI. Such efficient cooperation typically requires hardware and/or software to be added to one or more of the computers, which adds cost and complexity.
Other present systems depend on many procedural methods to improve the quality of the patient information combined into the MPI. The quality of the patient information varies widely due to such variables as the differences in data collected, admission processes, training, data available, individual usage and interface systems. Although the procedural methods improve the quality of the patient information in the MPI, the level of data quality cannot reach 100% because of the large number of variables, some related to human interaction.
Still other present systems combine the patient information from the multiple, different healthcare sources into a single view. The single view makes is very difficult to see the specific result in context of the original patient information. For example, if all of the blood pressure observations for a patient are combined, it is difficult to determine the medical context for a specific office visit, health problem, hospital stay, etc.
In light of these and other deficiencies, it would be desirable to have a HIS that accurately represents the patient information received from the multiple, different healthcare systems. Such a desirable HIS would have reasonable cost and complexity to permit small and medium sized healthcare providers to implement a system having the MPI. It would be desirable for the HIS to represent in real time all of the patient information available in the original format and context used by the multiple, different healthcare systems, without being confusing or complex. Accordingly, there is a need for a healthcare system and user interface for consolidating patient related information from different sources and corresponding method that would meet these and other desirable features of a healthcare information system.